{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[],"dockerImageVersionId":30627,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Overview\n\nText-to-speech (TTS) is the task of creating natural-sounding speech from text, where the speech can be generated in multiple languages and for multiple speakers. Several text-to-speech models are currently avaliable in Transformers, such as Bark, MMS, VITS and SpeechT5. We can easily generate audio using the `text-to-audio` pipeline (or its alias `text-to-speech`). Som models, like Bark, can also be conditioned to generate non-verbal communications such as laughing, and crying, or even add music.\n文本到语音（TTS）是将文本转换为自然语音的任务，生成的语音可以是多种语言和多个说话人。目前，Transformers 中提供了几种文本到语音模型，如 Bark、MMS、VITS 和 SpeechT5。我们可以使用 text-to-audio 管道（或其别名 text-to-speech）轻松生成音频。一些模型，如 Bark，还可以根据条件生成非语言交流，例如笑、哭，甚至添加音乐。","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"cell_type":"code","source":"%%capture\n!pip install transformers==4.35.2\n!pip install datasets==2.15.0\n!pip install soundfile==0.12.1\n!pip install speechbrain==0.5.16","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-15T01:29:05.305633Z","iopub.execute_input":"2025-08-15T01:29:05.306004Z","iopub.status.idle":"2025-08-15T01:29:39.543359Z","shell.execute_reply.started":"2025-08-15T01:29:05.305970Z","shell.execute_reply":"2025-08-15T01:29:39.542088Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"!cmake --version","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-15T01:30:23.551316Z","iopub.execute_input":"2025-08-15T01:30:23.552273Z","iopub.status.idle":"2025-08-15T01:30:24.695920Z","shell.execute_reply.started":"2025-08-15T01:30:23.552200Z","shell.execute_reply":"2025-08-15T01:30:24.694989Z"}},"outputs":[{"name":"stdout","text":"cmake: /opt/conda/lib/libcurl.so.4: no version information available (required by cmake)\ncmake version 3.22.1\n\nCMake suite maintained and supported by Kitware (kitware.com/cmake).\n","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"from transformers import pipeline\nimport os\n\n# 指定保存模型的路径\nmodel_save_path = \"./bark-small-tts\"\n\n# 1. 加载 pipeline（会自动下载模型）\nprint(\"正在下载并加载模型...\")\npipe = pipeline(\"text-to-speech\", model=\"suno/bark-small\", device=\"cuda\")\n\n# 2. 保存模型、tokenizer 和配置文件到本地\nprint(f\"正在将模型保存到 {model_save_path} ...\")\npipe.model.save_pretrained(model_save_path)\npipe.tokenizer.save_pretrained(model_save_path)\n# 如果有其他配置（如 generation_config），也一并保存\nif hasattr(pipe, \"generation_config\"):\n    pipe.generation_config.save_pretrained(model_save_path)\n\nprint(f\"模型已保存到 {model_save_path}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:26:50.554149Z","iopub.execute_input":"2025-08-13T08:26:50.555460Z","iopub.status.idle":"2025-08-13T08:27:03.957146Z","shell.execute_reply.started":"2025-08-13T08:26:50.555416Z","shell.execute_reply":"2025-08-13T08:27:03.955698Z"}},"outputs":[{"name":"stdout","text":"正在下载并加载模型...\n","output_type":"stream"},{"name":"stderr","text":"Removed shared tensor {'codec_model.encoder.layers.13.lstm.bias_hh_l1', 'codec_model.decoder.layers.1.lstm.bias_hh_l0', 'codec_model.encoder.layers.13.lstm.bias_ih_l1', 'codec_model.decoder.layers.1.lstm.bias_ih_l1', 'codec_model.decoder.layers.1.lstm.weight_hh_l0', 'codec_model.encoder.layers.13.lstm.bias_ih_l0', 'codec_model.encoder.layers.13.lstm.weight_ih_l1', 'codec_model.decoder.layers.1.lstm.weight_ih_l1', 'codec_model.decoder.layers.1.lstm.weight_hh_l1', 'codec_model.decoder.layers.1.lstm.bias_hh_l1', 'codec_model.encoder.layers.13.lstm.weight_hh_l1', 'codec_model.encoder.layers.13.lstm.weight_hh_l0', 'codec_model.decoder.layers.1.lstm.bias_ih_l0', 'codec_model.encoder.layers.13.lstm.bias_hh_l0'} while saving. This should be OK, but check by verifying that you don't receive any warning while reloading\n","output_type":"stream"},{"name":"stdout","text":"正在将模型保存到 ./bark-small-tts ...\n模型已保存到 ./bark-small-tts\n正在从本地路径重新加载 pipeline...\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.5` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n  warnings.warn(\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)","Cell \u001b[0;32mIn[11], line 24\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m# 3. （可选）从本地路径重新加载 pipeline\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;66;03m# 注意：Bark 模型可能需要指定 `add_prefix_space=True` 等 tokenizer 参数，但 pipeline 会自动处理\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m正在从本地路径重新加载 pipeline...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 24\u001b[0m pipe_from_local \u001b[38;5;241m=\u001b[39m \u001b[43mpipeline\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     25\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtext-to-speech\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     26\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_save_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     27\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcuda\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m     28\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;66;03m# 测试\u001b[39;00m\n\u001b[1;32m     31\u001b[0m text \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[clears throat] This is a test ... and I just took a long pause.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/pipelines/__init__.py:870\u001b[0m, in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m framework \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    869\u001b[0m     model_classes \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtf\u001b[39m\u001b[38;5;124m\"\u001b[39m: targeted_task[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtf\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m: targeted_task[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m]}\n\u001b[0;32m--> 870\u001b[0m     framework, model \u001b[38;5;241m=\u001b[39m \u001b[43minfer_framework_load_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    871\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    872\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_classes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_classes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    873\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m        \u001b[49m\u001b[43mframework\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mframework\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhub_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    880\u001b[0m model_config \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mconfig\n\u001b[1;32m    881\u001b[0m hub_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39m_commit_hash\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/pipelines/base.py:269\u001b[0m, in \u001b[0;36minfer_framework_load_model\u001b[0;34m(model, config, model_classes, task, framework, **model_kwargs)\u001b[0m\n\u001b[1;32m    263\u001b[0m     logger\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m    264\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel might be a PyTorch model (ending with `.bin`) but PyTorch is not available. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    265\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTrying to load the model with Tensorflow.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    266\u001b[0m     )\n\u001b[1;32m    268\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 269\u001b[0m     model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    270\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(model, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meval\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    271\u001b[0m         model \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39meval()\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py:566\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m    564\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(config) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m    565\u001b[0m     model_class \u001b[38;5;241m=\u001b[39m _get_model_class(config, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping)\n\u001b[0;32m--> 566\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    567\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhub_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    568\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    569\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    570\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnrecognized configuration class \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for this kind of AutoModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    571\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel type should be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(c\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    572\u001b[0m )\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/modeling_utils.py:3480\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   3471\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m dtype_orig \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3472\u001b[0m         torch\u001b[38;5;241m.\u001b[39mset_default_dtype(dtype_orig)\n\u001b[1;32m   3473\u001b[0m     (\n\u001b[1;32m   3474\u001b[0m         model,\n\u001b[1;32m   3475\u001b[0m         missing_keys,\n\u001b[1;32m   3476\u001b[0m         unexpected_keys,\n\u001b[1;32m   3477\u001b[0m         mismatched_keys,\n\u001b[1;32m   3478\u001b[0m         offload_index,\n\u001b[1;32m   3479\u001b[0m         error_msgs,\n\u001b[0;32m-> 3480\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load_pretrained_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3481\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3482\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3483\u001b[0m \u001b[43m        \u001b[49m\u001b[43mloaded_state_dict_keys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# XXX: rename?\u001b[39;49;00m\n\u001b[1;32m   3484\u001b[0m \u001b[43m        \u001b[49m\u001b[43mresolved_archive_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3485\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3486\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_mismatched_sizes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_mismatched_sizes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3487\u001b[0m \u001b[43m        \u001b[49m\u001b[43msharded_metadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msharded_metadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3488\u001b[0m \u001b[43m        \u001b[49m\u001b[43m_fast_init\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_fast_init\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3489\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlow_cpu_mem_usage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlow_cpu_mem_usage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3490\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3491\u001b[0m \u001b[43m        \u001b[49m\u001b[43moffload_folder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffload_folder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3492\u001b[0m \u001b[43m        \u001b[49m\u001b[43moffload_state_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffload_state_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3493\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3494\u001b[0m \u001b[43m        \u001b[49m\u001b[43mis_quantized\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mquantization_method\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mQuantizationMethod\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBITS_AND_BYTES\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3495\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeep_in_fp32_modules\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_in_fp32_modules\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3496\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3498\u001b[0m model\u001b[38;5;241m.\u001b[39mis_loaded_in_4bit \u001b[38;5;241m=\u001b[39m load_in_4bit\n\u001b[1;32m   3499\u001b[0m model\u001b[38;5;241m.\u001b[39mis_loaded_in_8bit \u001b[38;5;241m=\u001b[39m load_in_8bit\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/modeling_utils.py:3931\u001b[0m, in \u001b[0;36mPreTrainedModel._load_pretrained_model\u001b[0;34m(cls, model, state_dict, loaded_keys, resolved_archive_file, pretrained_model_name_or_path, ignore_mismatched_sizes, sharded_metadata, _fast_init, low_cpu_mem_usage, device_map, offload_folder, offload_state_dict, dtype, is_quantized, keep_in_fp32_modules)\u001b[0m\n\u001b[1;32m   3927\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize mismatch\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m error_msg:\n\u001b[1;32m   3928\u001b[0m         error_msg \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m   3929\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124mYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3930\u001b[0m         )\n\u001b[0;32m-> 3931\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError(s) in loading state_dict for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00merror_msg\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   3933\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_quantized:\n\u001b[1;32m   3934\u001b[0m     unexpected_keys \u001b[38;5;241m=\u001b[39m [elem \u001b[38;5;28;01mfor\u001b[39;00m elem \u001b[38;5;129;01min\u001b[39;00m unexpected_keys \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSCB\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m elem]\n","\u001b[0;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for BarkModel:\n\tsize mismatch for semantic.input_embeds_layer.weight: copying a param with shape torch.Size([129600, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for coarse_acoustics.input_embeds_layer.weight: copying a param with shape torch.Size([12096, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for coarse_acoustics.lm_head.weight: copying a param with shape torch.Size([12096, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.0.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.1.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.2.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.3.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.4.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.5.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.6.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.7.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.lm_heads.0.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.lm_heads.1.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.lm_heads.2.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.lm_heads.3.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.lm_heads.4.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.lm_heads.5.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.lm_heads.6.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method."],"ename":"RuntimeError","evalue":"Error(s) in loading state_dict for BarkModel:\n\tsize mismatch for semantic.input_embeds_layer.weight: copying a param with shape torch.Size([129600, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for coarse_acoustics.input_embeds_layer.weight: copying a param with shape torch.Size([12096, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for coarse_acoustics.lm_head.weight: copying a param with shape torch.Size([12096, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.0.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.1.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.2.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.3.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.4.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.5.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.6.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.input_embeds_layers.7.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.lm_heads.0.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.lm_heads.1.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.lm_heads.2.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.lm_heads.3.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.lm_heads.4.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.lm_heads.5.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tsize mismatch for fine_acoustics.lm_heads.6.weight: copying a param with shape torch.Size([1056, 768]) from checkpoint, the shape in current model is torch.Size([10048, 768]).\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method.","output_type":"error"}],"execution_count":11},{"cell_type":"code","source":"!pip install modelscope -i https://mirrors.aliyun.com/pypi/simple","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-14T06:21:28.580871Z","iopub.execute_input":"2025-08-14T06:21:28.581531Z","iopub.status.idle":"2025-08-14T06:21:43.195141Z","shell.execute_reply.started":"2025-08-14T06:21:28.581500Z","shell.execute_reply":"2025-08-14T06:21:43.194201Z"}},"outputs":[{"name":"stdout","text":"Collecting modelscope\n  Obtaining dependency information for modelscope from https://files.pythonhosted.org/packages/81/70/6cab7e0fc4316d1eec6425a2043e48a38a8bfbad6a3826e6140d41d83f5c/modelscope-1.28.2-py3-none-any.whl.metadata\n  Downloading modelscope-1.28.2-py3-none-any.whl.metadata (39 kB)\nRequirement already satisfied: requests>=2.25 in /opt/conda/lib/python3.10/site-packages (from modelscope) (2.31.0)\nRequirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from modelscope) (68.1.2)\nRequirement already satisfied: tqdm>=4.64.0 in /opt/conda/lib/python3.10/site-packages (from modelscope) (4.66.1)\nRequirement already satisfied: urllib3>=1.26 in /opt/conda/lib/python3.10/site-packages (from modelscope) (1.26.15)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests>=2.25->modelscope) (3.2.0)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests>=2.25->modelscope) (3.4)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests>=2.25->modelscope) (2023.11.17)\nDownloading modelscope-1.28.2-py3-none-any.whl (5.9 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.9/5.9 MB\u001b[0m \u001b[31m58.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hInstalling collected packages: modelscope\nSuccessfully installed modelscope-1.28.2\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"#SDK模型下载 如果你不是在外网 hugface上面的模型无法下载 可以使用这个\nfrom modelscope import snapshot_download\nimport os\nmd_p='./bark-small'\nif not os.path.exists(md_p):\n    os.mkdir(md_p)\nmodel_dir = snapshot_download('angelala00/bark-small',cache_dir=md_p)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-14T06:22:01.765923Z","iopub.execute_input":"2025-08-14T06:22:01.766672Z","iopub.status.idle":"2025-08-14T06:45:21.136510Z","shell.execute_reply.started":"2025-08-14T06:22:01.766639Z","shell.execute_reply":"2025-08-14T06:45:21.135876Z"}},"outputs":[{"name":"stdout","text":"Downloading Model from https://www.modelscope.cn to directory: ./bark-small/angelala00/bark-small\n","output_type":"stream"},{"name":"stderr","text":"2025-08-14 06:22:11,009 - modelscope - INFO - Got 792 files, start to download ...\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Processing 792 items:   0%|          | 0.00/792 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"942ac5f000be4485805cfa9816ed78bf"}},"metadata":{}},{"name":"stderr","text":"\nDownloading [speaker_embeddings/announcer_coarse_prompt.npy]:   0%|          | 0.00/3.05k [00:00<?, ?B/s]\u001b[A\n\n\n\nDownloading [config.json]:   0%|          | 0.00/8.60k [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\u001b[A\n\n\nDownloading [speaker_embeddings/announcer_semantic_prompt.npy]:   0%|          | 0.00/1.10k [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\n\nDownloading [speaker_embeddings/announcer_fine_prompt.npy]:   0%|          | 0.00/11.8k [00:00<?, ?B/s]\u001b[A\u001b[A\n\n\n\n\nDownloading 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以下文本转语音代码可以使用：\nfrom transformers import pipeline\nimport numpy as np\nimport scipy\n# example of pipelien text-to-speech\n# pipe=pipeline(\"text-to-speech\", model=\"suno/bark-small\", device='cuda')\n# text=\"[clears throat] This is a test ...  and I just took a long pause.\"\n# output=pipe(text)\nmodel_path = \"./bark-small/angelala00/bark-small\"\npipe = pipeline(\"text-to-speech\", model=model_path, device='cuda')\ntext=\"[clears throat] This is a test ...  and I just took a long pause.\"\noutput=pipe(text)\n# Step 3: 提取音频数据和采样率\naudio = output[\"audio\"]\nsampling_rate = output[\"sampling_rate\"]\n\n# ✅ 关键修复：处理音频维度\naudio = np.array(audio)          # 确保是 numpy 数组\naudio = audio.squeeze()          # 去除所有长度为1的维度\n\n# 确保是 1D 或 2D\nif audio.ndim == 1:\n    print(f\"Audio is mono, shape: {audio.shape}\")\nelif audio.ndim == 2:\n    if audio.shape[1] > 2:\n        print(f\"Trimming audio from {audio.shape[1]} channels to 2\")\n        audio = audio[:, :2]\n    print(f\"Audio is stereo, shape: {audio.shape}\")\nelse:\n    raise ValueError(f\"Unsupported audio dimensions: {audio.shape}\")\n\n# 转为 float32\naudio = audio.astype(np.float32)\n\n# 保存\noutput_file = \"output_audio.wav\"\nprint(f\"Saving audio to {output_file}...\")\nscipy.io.wavfile.write(output_file, rate=sampling_rate, data=audio)\nprint(f\"✅ Audio saved to '{output_file}'\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-15T01:31:12.266442Z","iopub.execute_input":"2025-08-15T01:31:12.266805Z","iopub.status.idle":"2025-08-15T01:31:17.004425Z","shell.execute_reply.started":"2025-08-15T01:31:12.266776Z","shell.execute_reply":"2025-08-15T01:31:17.003107Z"}},"outputs":[{"name":"stderr","text":"Exception ignored in: <function _xla_gc_callback at 0x7b9704a789d0>\nTraceback (most recent call last):\n  File \"/opt/conda/lib/python3.10/site-packages/jax/_src/lib/__init__.py\", line 97, in _xla_gc_callback\n    def _xla_gc_callback(*args):\nKeyboardInterrupt: \nException ignored in: <function _xla_gc_callback at 0x7b9704a789d0>\nTraceback (most recent call last):\n  File \"/opt/conda/lib/python3.10/site-packages/jax/_src/lib/__init__.py\", line 97, in _xla_gc_callback\n    def _xla_gc_callback(*args):\nKeyboardInterrupt: \n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#如果是基于modelscope本地跑模型的 以下文本转语音代码可以使用：\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pipeline\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\n","File \u001b[0;32m<frozen importlib._bootstrap>:1075\u001b[0m, in \u001b[0;36m_handle_fromlist\u001b[0;34m(module, fromlist, import_, recursive)\u001b[0m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/utils/import_utils.py:1372\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1368\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdirect_transformers_import\u001b[39m(path: \u001b[38;5;28mstr\u001b[39m, file\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__init__.py\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ModuleType:\n\u001b[1;32m   1369\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Imports transformers directly\u001b[39;00m\n\u001b[1;32m   1370\u001b[0m \n\u001b[1;32m   1371\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m-> 1372\u001b[0m \u001b[38;5;124;03m        path (`str`): The path to the source file\u001b[39;00m\n\u001b[1;32m   1373\u001b[0m \u001b[38;5;124;03m        file (`str`, optional): The file to join with the path. Defaults to \"__init__.py\".\u001b[39;00m\n\u001b[1;32m   1374\u001b[0m \n\u001b[1;32m   1375\u001b[0m \u001b[38;5;124;03m    Returns:\u001b[39;00m\n\u001b[1;32m   1376\u001b[0m \u001b[38;5;124;03m        `ModuleType`: The resulting imported module\u001b[39;00m\n\u001b[1;32m   1377\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m   1378\u001b[0m     name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtransformers\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1379\u001b[0m     location \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(path, file)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/utils/import_utils.py:1382\u001b[0m, in \u001b[0;36m_get_module\u001b[0;34m(self, module_name)\u001b[0m\n\u001b[1;32m   1380\u001b[0m spec \u001b[38;5;241m=\u001b[39m importlib\u001b[38;5;241m.\u001b[39mutil\u001b[38;5;241m.\u001b[39mspec_from_file_location(name, location, submodule_search_locations\u001b[38;5;241m=\u001b[39m[path])\n\u001b[1;32m   1381\u001b[0m module \u001b[38;5;241m=\u001b[39m importlib\u001b[38;5;241m.\u001b[39mutil\u001b[38;5;241m.\u001b[39mmodule_from_spec(spec)\n\u001b[0;32m-> 1382\u001b[0m spec\u001b[38;5;241m.\u001b[39mloader\u001b[38;5;241m.\u001b[39mexec_module(module)\n\u001b[1;32m   1383\u001b[0m module \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mmodules[name]\n\u001b[1;32m   1384\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\n","File \u001b[0;32m/opt/conda/lib/python3.10/importlib/__init__.py:126\u001b[0m, in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m    125\u001b[0m         level \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 126\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/pipelines/__init__.py:28\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdynamic_module_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_class_from_dynamic_module\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeature_extraction_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PreTrainedFeatureExtractor\n\u001b[0;32m---> 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimage_processing_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BaseImageProcessor\n\u001b[1;32m     29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauto\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfiguration_auto\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AutoConfig\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauto\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeature_extraction_auto\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/image_processing_utils.py:28\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdynamic_module_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m custom_object_save\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeature_extraction_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BatchFeature \u001b[38;5;28;01mas\u001b[39;00m BaseBatchFeature\n\u001b[0;32m---> 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimage_transforms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m center_crop, normalize, rescale\n\u001b[1;32m     29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimage_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ChannelDimension\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     31\u001b[0m     IMAGE_PROCESSOR_NAME,\n\u001b[1;32m     32\u001b[0m     PushToHubMixin,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     40\u001b[0m     logging,\n\u001b[1;32m     41\u001b[0m )\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/image_transforms.py:47\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_tf_available():\n\u001b[0;32m---> 47\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_flax_available():\n\u001b[1;32m     50\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mjnp\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/__init__.py:38\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_typing\u001b[39;00m\n\u001b[0;32m---> 38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m module_util \u001b[38;5;28;01mas\u001b[39;00m _module_util\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlazy_loader\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LazyLoader \u001b[38;5;28;01mas\u001b[39;00m _LazyLoader\n\u001b[1;32m     41\u001b[0m \u001b[38;5;66;03m# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/python/__init__.py:42\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01meager\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m context\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# pylint: enable=wildcard-import\u001b[39;00m\n\u001b[1;32m     40\u001b[0m \n\u001b[1;32m     41\u001b[0m \u001b[38;5;66;03m# Bring in subpackages.\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m data\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m distribute\n\u001b[1;32m     44\u001b[0m \u001b[38;5;66;03m# from tensorflow.python import keras\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/python/data/__init__.py:21\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;124;03m\"\"\"`tf.data.Dataset` API for input pipelines.\u001b[39;00m\n\u001b[1;32m     16\u001b[0m \n\u001b[1;32m     17\u001b[0m \u001b[38;5;124;03mSee [Importing Data](https://tensorflow.org/guide/data) for an overview.\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# pylint: disable=unused-import\u001b[39;00m\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m experimental\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdataset_ops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AUTOTUNE\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdataset_ops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Dataset\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/python/data/experimental/__init__.py:97\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;124;03m\"\"\"Experimental API for building input pipelines.\u001b[39;00m\n\u001b[1;32m     16\u001b[0m \n\u001b[1;32m     17\u001b[0m \u001b[38;5;124;03mThis module contains experimental `Dataset` sources and transformations that can\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;124;03m@@UNKNOWN_CARDINALITY\u001b[39;00m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     96\u001b[0m \u001b[38;5;66;03m# pylint: disable=unused-import\u001b[39;00m\n\u001b[0;32m---> 97\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m service\n\u001b[1;32m     98\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbatching\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dense_to_ragged_batch\n\u001b[1;32m     99\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbatching\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dense_to_sparse_batch\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/python/data/experimental/service/__init__.py:419\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2020 The TensorFlow Authors. All Rights Reserved.\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;66;03m# ==============================================================================\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;124;03m\"\"\"API for using the tf.data service.\u001b[39;00m\n\u001b[1;32m     16\u001b[0m \n\u001b[1;32m     17\u001b[0m \u001b[38;5;124;03mThis module contains:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[38;5;124;03m  job of ParameterServerStrategy).\u001b[39;00m\n\u001b[1;32m    417\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 419\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata_service_ops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m distribute\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata_service_ops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m from_dataset_id\n\u001b[1;32m    421\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata_service_ops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m register_dataset\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/python/data/experimental/ops/data_service_ops.py:22\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprotobuf\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m data_service_pb2\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tf2\n\u001b[0;32m---> 22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compression_ops\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mservice\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _pywrap_server_lib\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mservice\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _pywrap_utils\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/python/data/experimental/ops/compression_ops.py:16\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2020 The TensorFlow Authors. All Rights Reserved.\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;66;03m# ==============================================================================\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;124;03m\"\"\"Ops for compressing and uncompressing dataset elements.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m structure\n\u001b[1;32m     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gen_resource_variable_ops\n\u001b[0;32m---> 50\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m gen_state_ops\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m handle_data_util\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m math_ops\n","File \u001b[0;32m<frozen importlib._bootstrap>:1027\u001b[0m, in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n","File \u001b[0;32m<frozen importlib._bootstrap>:1006\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n","File \u001b[0;32m<frozen importlib._bootstrap>:688\u001b[0m, in \u001b[0;36m_load_unlocked\u001b[0;34m(spec)\u001b[0m\n","File \u001b[0;32m<frozen importlib._bootstrap_external>:879\u001b[0m, in \u001b[0;36mexec_module\u001b[0;34m(self, module)\u001b[0m\n","File \u001b[0;32m<frozen importlib._bootstrap_external>:1016\u001b[0m, in \u001b[0;36mget_code\u001b[0;34m(self, fullname)\u001b[0m\n","File \u001b[0;32m<frozen importlib._bootstrap_external>:1074\u001b[0m, in \u001b[0;36mget_data\u001b[0;34m(self, path)\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}],"execution_count":7},{"cell_type":"code","source":"# !tar -cf bark-small-tts.tar bark-small-tts","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:29:39.601294Z","iopub.execute_input":"2025-08-13T08:29:39.602185Z","iopub.status.idle":"2025-08-13T08:29:42.463517Z","shell.execute_reply.started":"2025-08-13T08:29:39.602153Z","shell.execute_reply":"2025-08-13T08:29:42.462528Z"}},"outputs":[{"name":"stderr","text":"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n","output_type":"stream"}],"execution_count":12},{"cell_type":"code","source":"from transformers import pipeline\n\n# example of pipelien text-to-speech\npipe=pipeline(\"text-to-speech\", model=\"suno/bark-small\", device='cuda')\ntext=\"[clears throat] This is a test ...  and I just took a long pause.\"\noutput=pipe(text)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-15T01:31:30.086386Z","iopub.execute_input":"2025-08-15T01:31:30.087146Z","iopub.status.idle":"2025-08-15T01:32:17.565997Z","shell.execute_reply.started":"2025-08-15T01:31:30.087115Z","shell.execute_reply":"2025-08-15T01:32:17.565182Z"}},"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"config.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"63e0ac03793e4fc4b75784fc2ce21b4f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"pytorch_model.bin:   0%|          | 0.00/1.68G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5e4d9911fdfe48feb0ffea75af2e048d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"generation_config.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f139aca0c85f46759296fc1bfba08d71"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/353 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5f47ae908a8f444fa913f63325d9812c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"vocab.txt: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"79ca453d25c64790b261e3471ed37e66"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c6fb757ba85f4b34a5247de23a705156"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6f16392531ae4960a83b713881ae6429"}},"metadata":{}},{"name":"stderr","text":"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\nSetting `pad_token_id` to `eos_token_id`:10000 for open-end generation.\n","output_type":"stream"}],"execution_count":8},{"cell_type":"code","source":"from IPython.display import Audio\n\nAudio(output[\"audio\"], rate=output[\"sampling_rate\"])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-15T01:32:44.371130Z","iopub.execute_input":"2025-08-15T01:32:44.371812Z","iopub.status.idle":"2025-08-15T01:32:44.398382Z","shell.execute_reply.started":"2025-08-15T01:32:44.371781Z","shell.execute_reply":"2025-08-15T01:32:44.397616Z"}},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"<IPython.lib.display.Audio object>","text/html":"\n                <audio  controls=\"controls\" >\n                    <source 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\" type=\"audio/wav\" />\n                    Your browser does not support the audio element.\n                </audio>\n              "},"metadata":{}}],"execution_count":9},{"cell_type":"markdown","source":"We are goging to fine-tune a TTS model, we can currently fine-tune SpeechT5. It is pre-trained on a combination of `speech-to-text` and `text-to-speech` data, allowing it to learn a unified space of hidden representations shared by both text and speech. This means that the same pre-trained model can be fine-tuned for different tasks. Furthermore, SpeechT5 supports multiple speakers through x-vector speaker embeddings.\n\n我们将对一个语音合成（TTS）模型进行微调，目前我们可以微调 SpeechT5 模型。该模型在语音到文本（speech-to-text）和文本到语音（text-to-speech）数据的组合上进行了预训练，使其能够学习文本和语音共有的统一隐藏表示空间。这意味着同一个预训练模型可以针对不同任务进行微调。此外，SpeechT5 通过 x-vector 说话人嵌入（speaker embeddings）支持多人语音合成。","metadata":{}},{"cell_type":"code","source":"import os\nfrom huggingface_hub import login\nfrom kaggle_secrets import UserSecretsClient\n\nuser_secrets = UserSecretsClient()\n\nlogin(token=user_secrets.get_secret(\"HUGGINGFACE_TOKEN\"))\n\nos.environ[\"WANDB_API_KEY\"]=user_secrets.get_secret(\"WANDB_API_KEY\")\nos.environ[\"WANDB_PROJECT\"] = \"Fine-tuning speech-t5\"\nos.environ[\"WANDB_NOTES\"] = \"Fine tune model distilbert base uncased\"\nos.environ[\"WANDB_NAME\"] = \"ft-speech-t5-on-voxpopuli\"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-15T01:33:14.491167Z","iopub.execute_input":"2025-08-15T01:33:14.491708Z","iopub.status.idle":"2025-08-15T01:33:14.955811Z","shell.execute_reply.started":"2025-08-15T01:33:14.491673Z","shell.execute_reply":"2025-08-15T01:33:14.954571Z"}},"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mBackendError\u001b[0m                              Traceback (most recent call last)","Cell \u001b[0;32mIn[10], line 7\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkaggle_secrets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m UserSecretsClient\n\u001b[1;32m      5\u001b[0m user_secrets \u001b[38;5;241m=\u001b[39m UserSecretsClient()\n\u001b[0;32m----> 7\u001b[0m login(token\u001b[38;5;241m=\u001b[39m\u001b[43muser_secrets\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_secret\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHUGGINGFACE_TOKEN\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m      9\u001b[0m os\u001b[38;5;241m.\u001b[39menviron[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWANDB_API_KEY\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m=\u001b[39muser_secrets\u001b[38;5;241m.\u001b[39mget_secret(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWANDB_API_KEY\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     10\u001b[0m os\u001b[38;5;241m.\u001b[39menviron[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWANDB_PROJECT\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFine-tuning speech-t5\u001b[39m\u001b[38;5;124m\"\u001b[39m\n","File \u001b[0;32m~/.local/lib/python3.10/site-packages/kaggle_secrets.py:64\u001b[0m, in \u001b[0;36mUserSecretsClient.get_secret\u001b[0;34m(self, label)\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ValidationError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLabel must be non-empty.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     61\u001b[0m request_body \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     62\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLabel\u001b[39m\u001b[38;5;124m'\u001b[39m: label,\n\u001b[1;32m     63\u001b[0m }\n\u001b[0;32m---> 64\u001b[0m response_json \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweb_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmake_post_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGET_USER_SECRET_BY_LABEL_ENDPOINT\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msecret\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m response_json:\n\u001b[1;32m     66\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m BackendError(\n\u001b[1;32m     67\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnexpected response from the service. Response: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresponse_json\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n","File \u001b[0;32m~/.local/lib/python3.10/site-packages/kaggle_web_client.py:49\u001b[0m, in \u001b[0;36mKaggleWebClient.make_post_request\u001b[0;34m(self, data, endpoint, timeout)\u001b[0m\n\u001b[1;32m     47\u001b[0m         response_json \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(response\u001b[38;5;241m.\u001b[39mread())\n\u001b[1;32m     48\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m response_json\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwasSuccessful\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresult\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m response_json:\n\u001b[0;32m---> 49\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m BackendError(\n\u001b[1;32m     50\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnexpected response from the service. Response: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresponse_json\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     51\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m response_json[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresult\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (URLError, socket\u001b[38;5;241m.\u001b[39mtimeout) \u001b[38;5;28;01mas\u001b[39;00m e:\n","\u001b[0;31mBackendError\u001b[0m: Unexpected response from the service. Response: {'errors': ['No user secrets exist for kernel id 88562787 and label HUGGINGFACE_TOKEN.'], 'error': {'code': 5}, 'wasSuccessful': False}."],"ename":"BackendError","evalue":"Unexpected response from the service. Response: {'errors': ['No user secrets exist for kernel id 88562787 and label HUGGINGFACE_TOKEN.'], 'error': {'code': 5}, 'wasSuccessful': False}.","output_type":"error"}],"execution_count":10},{"cell_type":"markdown","source":"# Loading the dataset\n加载数据集\n\nHere we are going to use VoxPopuli dataset. It is a large-scale multilingual speech corpus consisting of data sourced from 2009-2020 European Parliament event recordings. It contains labelled audio-transcription data for 15 European languages.\n\n**Note that VoxPopuli or any other automated speech recognition(ASR) dataset may not be the most suitable option for training TTS models. The features that make it beneficial for ASR, such as excessive background noise, are typically undesirable in TTS. However, finding top-quality, multilingual, and multi-speaker TTS datasets can be quite challenging.**\n\n\n\n我们将使用 VoxPopuli 数据集。这是一个大规模的多语言语音语料库，数据来源于 2009 年至 2020 年欧洲议会活动的录音。它包含 15 种欧洲语言的带标注音频-文本转录数据。\n\n请注意，VoxPopuli 或其他任何自动语音识别（ASR）数据集可能并不是训练语音合成（TTS）模型的最佳选择。因为适合 ASR 的一些特征（例如过多的背景噪音）在 TTS 中通常是不理想的。然而，要找到高质量、多语言且包含多位说话人的 TTS 数据集是非常困难的。","metadata":{}},{"cell_type":"code","source":"#将数据及保存到本地\nfrom datasets import load_dataset\n\n# 1. 加载数据集\ndataset = load_dataset(\"facebook/voxpopuli\", \"nl\", split=\"train\")\n\n# 2. 将数据集保存到本地指定目录\ndataset.save_to_disk(\"./voxpopuli_nl_train\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T01:50:37.850308Z","iopub.execute_input":"2025-08-19T01:50:37.850627Z","iopub.status.idle":"2025-08-19T01:56:48.560848Z","shell.execute_reply.started":"2025-08-19T01:50:37.850599Z","shell.execute_reply":"2025-08-19T01:56:48.560139Z"}},"outputs":[{"name":"stdout","text":"Downloading and preparing dataset voxpopuli/nl to /root/.cache/huggingface/datasets/facebook___voxpopuli/nl/1.3.0/b5ff837284f0778eefe0f642734e142d8c3f574eba8c9c8a4b13602297f73604...\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ace096aa5f584b969271b0a9fed136ae"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"18b0962621c94b0685abeef5ada83de9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"768cdbe154a143a2b17039b5260e2bde"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading data:   0%|          | 0.00/1.35G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2bd541dbc79042b69fcfd330df4a5a35"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading data:   0%|          | 0.00/1.34G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7b17ffe740b84980a5dec2e1b9250d9a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading data:   0%|          | 0.00/255M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d34814bb7e3f46dea8ae8cbfacffe3b5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading data:   0%|          | 0.00/321M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4f5e41249cf7467eb2750d04a735d43e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading data:   0%|          | 0.00/319M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fce270d6265b495c855bdc492f6d37a2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"689b7d5ea61945d1925e9e13ae3d1c04"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Generating train split: 0 examples [00:00, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Generating validation split: 0 examples [00:00, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Generating test split: 0 examples [00:00, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"name":"stdout","text":"Dataset voxpopuli downloaded and prepared to /root/.cache/huggingface/datasets/facebook___voxpopuli/nl/1.3.0/b5ff837284f0778eefe0f642734e142d8c3f574eba8c9c8a4b13602297f73604. Subsequent calls will reuse this data.\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"from datasets import load_dataset, Audio\n\ndataset=load_dataset(\"facebook/voxpopuli\",\"nl\", split=\"train\")\ndataset","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T01:49:28.823984Z","iopub.execute_input":"2025-08-19T01:49:28.824212Z","iopub.status.idle":"2025-08-19T01:50:35.451698Z","shell.execute_reply.started":"2025-08-19T01:49:28.824188Z","shell.execute_reply":"2025-08-19T01:50:35.449901Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Downloading builder script: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3dfa2ec1d51045d28a82217391adf5ba"}},"metadata":{}},{"name":"stdout","text":"Downloading and preparing dataset voxpopuli/nl to /root/.cache/huggingface/datasets/facebook___voxpopuli/nl/1.3.0/b5ff837284f0778eefe0f642734e142d8c3f574eba8c9c8a4b13602297f73604...\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Downloading data: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e24420d8092843b29fa291277822aeaa"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8f059e9aad3e47689c5a5ed209b41b67"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading data:   0%|          | 0.00/7.09M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"daf71907170b4477993b14beb234f619"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading data:   0%|          | 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?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"34e99ffb75204950ae90e6d4fde41a3e"}},"metadata":{}},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdatasets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_dataset, Audio\n\u001b[0;32m----> 3\u001b[0m dataset\u001b[38;5;241m=\u001b[39m\u001b[43mload_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfacebook/voxpopuli\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnl\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msplit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtrain\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m dataset\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/load.py:1691\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)\u001b[0m\n\u001b[1;32m   1688\u001b[0m try_from_hf_gcs \u001b[38;5;241m=\u001b[39m path \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _PACKAGED_DATASETS_MODULES\n\u001b[1;32m   1690\u001b[0m \u001b[38;5;66;03m# Download and prepare data\u001b[39;00m\n\u001b[0;32m-> 1691\u001b[0m \u001b[43mbuilder_instance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1692\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1693\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1694\u001b[0m \u001b[43m    \u001b[49m\u001b[43mignore_verifications\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_verifications\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1695\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1696\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_auth_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_auth_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1697\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1699\u001b[0m \u001b[38;5;66;03m# Build dataset for splits\u001b[39;00m\n\u001b[1;32m   1700\u001b[0m keep_in_memory \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m   1701\u001b[0m     keep_in_memory \u001b[38;5;28;01mif\u001b[39;00m keep_in_memory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m is_small_dataset(builder_instance\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size)\n\u001b[1;32m   1702\u001b[0m )\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/builder.py:605\u001b[0m, in \u001b[0;36mDatasetBuilder.download_and_prepare\u001b[0;34m(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m    603\u001b[0m         logger\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHF google storage unreachable. Downloading and preparing it from source\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    604\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m downloaded_from_gcs:\n\u001b[0;32m--> 605\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    606\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify_infos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdownload_and_prepare_kwargs\u001b[49m\n\u001b[1;32m    607\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    608\u001b[0m \u001b[38;5;66;03m# Sync info\u001b[39;00m\n\u001b[1;32m    609\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(split\u001b[38;5;241m.\u001b[39mnum_bytes \u001b[38;5;28;01mfor\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39msplits\u001b[38;5;241m.\u001b[39mvalues())\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/builder.py:1104\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verify_infos)\u001b[0m\n\u001b[1;32m   1103\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_download_and_prepare\u001b[39m(\u001b[38;5;28mself\u001b[39m, dl_manager, verify_infos):\n\u001b[0;32m-> 1104\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_duplicate_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_infos\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/builder.py:672\u001b[0m, in \u001b[0;36mDatasetBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verify_infos, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m    670\u001b[0m split_dict \u001b[38;5;241m=\u001b[39m SplitDict(dataset_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m    671\u001b[0m split_generators_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_split_generators_kwargs(prepare_split_kwargs)\n\u001b[0;32m--> 672\u001b[0m split_generators \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_split_generators\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msplit_generators_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    674\u001b[0m \u001b[38;5;66;03m# Checksums verification\u001b[39;00m\n\u001b[1;32m    675\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verify_infos:\n","File \u001b[0;32m~/.cache/huggingface/modules/datasets_modules/datasets/facebook--voxpopuli/b5ff837284f0778eefe0f642734e142d8c3f574eba8c9c8a4b13602297f73604/voxpopuli.py:143\u001b[0m, in \u001b[0;36mVoxpopuli._split_generators\u001b[0;34m(self, dl_manager)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[38;5;66;03m# dl_manager.download_config.num_proc = len(urls)\u001b[39;00m\n\u001b[1;32m    142\u001b[0m meta_paths \u001b[38;5;241m=\u001b[39m dl_manager\u001b[38;5;241m.\u001b[39mdownload_and_extract(meta_urls)\n\u001b[0;32m--> 143\u001b[0m audio_paths \u001b[38;5;241m=\u001b[39m \u001b[43mdl_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload\u001b[49m\u001b[43m(\u001b[49m\u001b[43maudio_urls\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    145\u001b[0m local_extracted_audio_paths \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    146\u001b[0m     dl_manager\u001b[38;5;241m.\u001b[39mextract(audio_paths) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dl_manager\u001b[38;5;241m.\u001b[39mis_streaming \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[1;32m    147\u001b[0m     {\n\u001b[1;32m    148\u001b[0m         split: {lang: [\u001b[38;5;28;01mNone\u001b[39;00m] \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mlen\u001b[39m(audio_paths[split][lang]) \u001b[38;5;28;01mfor\u001b[39;00m lang \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mlanguages} \u001b[38;5;28;01mfor\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m splits\n\u001b[1;32m    149\u001b[0m     }\n\u001b[1;32m    150\u001b[0m )\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124men_accented\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/utils/download_manager.py:282\u001b[0m, in \u001b[0;36mDownloadManager.download\u001b[0;34m(self, url_or_urls)\u001b[0m\n\u001b[1;32m    279\u001b[0m download_func \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_download, download_config\u001b[38;5;241m=\u001b[39mdownload_config)\n\u001b[1;32m    281\u001b[0m start_time \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mnow()\n\u001b[0;32m--> 282\u001b[0m downloaded_path_or_paths \u001b[38;5;241m=\u001b[39m \u001b[43mmap_nested\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    283\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    284\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl_or_urls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    285\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmap_tuple\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    286\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    287\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdisable_tqdm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mis_progress_bar_enabled\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    288\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdesc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mDownloading data files\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    289\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    290\u001b[0m duration \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mnow() \u001b[38;5;241m-\u001b[39m start_time\n\u001b[1;32m    291\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownloading took \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mduration\u001b[38;5;241m.\u001b[39mtotal_seconds()\u001b[38;5;250m \u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;241m60\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m min\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/utils/py_utils.py:314\u001b[0m, in \u001b[0;36mmap_nested\u001b[0;34m(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)\u001b[0m\n\u001b[1;32m    312\u001b[0m     num_proc \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_proc \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(iterable) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m num_proc:\n\u001b[0;32m--> 314\u001b[0m     mapped \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    315\u001b[0m         _single_map_nested((function, obj, types, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    316\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m logging\u001b[38;5;241m.\u001b[39mtqdm(iterable, disable\u001b[38;5;241m=\u001b[39mdisable_tqdm, desc\u001b[38;5;241m=\u001b[39mdesc)\n\u001b[1;32m    317\u001b[0m     ]\n\u001b[1;32m    318\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    319\u001b[0m     split_kwds \u001b[38;5;241m=\u001b[39m []  \u001b[38;5;66;03m# We organize the splits ourselve (contiguous splits)\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/utils/py_utils.py:315\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    312\u001b[0m     num_proc \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_proc \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(iterable) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m num_proc:\n\u001b[1;32m    314\u001b[0m     mapped \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m--> 315\u001b[0m         \u001b[43m_single_map_nested\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    316\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m logging\u001b[38;5;241m.\u001b[39mtqdm(iterable, disable\u001b[38;5;241m=\u001b[39mdisable_tqdm, desc\u001b[38;5;241m=\u001b[39mdesc)\n\u001b[1;32m    317\u001b[0m     ]\n\u001b[1;32m    318\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    319\u001b[0m     split_kwds \u001b[38;5;241m=\u001b[39m []  \u001b[38;5;66;03m# We organize the splits ourselve (contiguous splits)\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/utils/py_utils.py:267\u001b[0m, in \u001b[0;36m_single_map_nested\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m    264\u001b[0m pbar \u001b[38;5;241m=\u001b[39m logging\u001b[38;5;241m.\u001b[39mtqdm(pbar_iterable, disable\u001b[38;5;241m=\u001b[39mdisable_tqdm, position\u001b[38;5;241m=\u001b[39mrank, unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobj\u001b[39m\u001b[38;5;124m\"\u001b[39m, desc\u001b[38;5;241m=\u001b[39mpbar_desc)\n\u001b[1;32m    266\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_struct, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m--> 267\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {k: _single_map_nested((function, v, types, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m)) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m pbar}\n\u001b[1;32m    268\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    269\u001b[0m     mapped \u001b[38;5;241m=\u001b[39m [_single_map_nested((function, v, types, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m)) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m pbar]\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/utils/py_utils.py:267\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    264\u001b[0m pbar \u001b[38;5;241m=\u001b[39m logging\u001b[38;5;241m.\u001b[39mtqdm(pbar_iterable, disable\u001b[38;5;241m=\u001b[39mdisable_tqdm, position\u001b[38;5;241m=\u001b[39mrank, unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobj\u001b[39m\u001b[38;5;124m\"\u001b[39m, desc\u001b[38;5;241m=\u001b[39mpbar_desc)\n\u001b[1;32m    266\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_struct, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m--> 267\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {k: \u001b[43m_single_map_nested\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m pbar}\n\u001b[1;32m    268\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    269\u001b[0m     mapped \u001b[38;5;241m=\u001b[39m [_single_map_nested((function, v, types, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m)) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m pbar]\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/utils/py_utils.py:269\u001b[0m, in \u001b[0;36m_single_map_nested\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {k: _single_map_nested((function, v, types, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m)) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m pbar}\n\u001b[1;32m    268\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 269\u001b[0m     mapped \u001b[38;5;241m=\u001b[39m [_single_map_nested((function, v, types, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m)) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m pbar]\n\u001b[1;32m    270\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_struct, \u001b[38;5;28mlist\u001b[39m):\n\u001b[1;32m    271\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m mapped\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/utils/py_utils.py:269\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {k: _single_map_nested((function, v, types, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m)) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m pbar}\n\u001b[1;32m    268\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 269\u001b[0m     mapped \u001b[38;5;241m=\u001b[39m [\u001b[43m_single_map_nested\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m pbar]\n\u001b[1;32m    270\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_struct, \u001b[38;5;28mlist\u001b[39m):\n\u001b[1;32m    271\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m mapped\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/utils/py_utils.py:251\u001b[0m, in \u001b[0;36m_single_map_nested\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;66;03m# Singleton first to spare some computation\u001b[39;00m\n\u001b[1;32m    250\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_struct, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_struct, types):\n\u001b[0;32m--> 251\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_struct\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[38;5;66;03m# Reduce logging to keep things readable in multiprocessing with tqdm\u001b[39;00m\n\u001b[1;32m    254\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m rank \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m logging\u001b[38;5;241m.\u001b[39mget_verbosity() \u001b[38;5;241m<\u001b[39m logging\u001b[38;5;241m.\u001b[39mWARNING:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/utils/download_manager.py:308\u001b[0m, in \u001b[0;36mDownloadManager._download\u001b[0;34m(self, url_or_filename, download_config)\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_relative_path(url_or_filename):\n\u001b[1;32m    306\u001b[0m     \u001b[38;5;66;03m# append the relative path to the base_path\u001b[39;00m\n\u001b[1;32m    307\u001b[0m     url_or_filename \u001b[38;5;241m=\u001b[39m url_or_path_join(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_base_path, url_or_filename)\n\u001b[0;32m--> 308\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcached_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py:234\u001b[0m, in \u001b[0;36mcached_path\u001b[0;34m(url_or_filename, download_config, **download_kwargs)\u001b[0m\n\u001b[1;32m    230\u001b[0m     url_or_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(url_or_filename)\n\u001b[1;32m    232\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_remote_url(url_or_filename):\n\u001b[1;32m    233\u001b[0m     \u001b[38;5;66;03m# URL, so get it from the cache (downloading if necessary)\u001b[39;00m\n\u001b[0;32m--> 234\u001b[0m     output_path \u001b[38;5;241m=\u001b[39m \u001b[43mget_from_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    235\u001b[0m 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\u001b[0;36mhttp_get\u001b[0;34m(url, temp_file, proxies, resume_size, headers, cookies, timeout, max_retries, desc)\u001b[0m\n\u001b[1;32m    420\u001b[0m total \u001b[38;5;241m=\u001b[39m resume_size \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mint\u001b[39m(content_length) \u001b[38;5;28;01mif\u001b[39;00m content_length \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    421\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m logging\u001b[38;5;241m.\u001b[39mtqdm(\n\u001b[1;32m    422\u001b[0m     unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mB\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    423\u001b[0m     unit_scale\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    427\u001b[0m     disable\u001b[38;5;241m=\u001b[39m\u001b[38;5;129;01mnot\u001b[39;00m logging\u001b[38;5;241m.\u001b[39mis_progress_bar_enabled(),\n\u001b[1;32m    428\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m progress:\n\u001b[0;32m--> 429\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m response\u001b[38;5;241m.\u001b[39miter_content(chunk_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m):\n\u001b[1;32m    430\u001b[0m         progress\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mlen\u001b[39m(chunk))\n\u001b[1;32m    431\u001b[0m         temp_file\u001b[38;5;241m.\u001b[39mwrite(chunk)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/requests/models.py:816\u001b[0m, in \u001b[0;36mResponse.iter_content.<locals>.generate\u001b[0;34m()\u001b[0m\n\u001b[1;32m    814\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    815\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 816\u001b[0m         \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw\u001b[38;5;241m.\u001b[39mstream(chunk_size, decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    817\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m ProtocolError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    818\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ChunkedEncodingError(e)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/urllib3/response.py:628\u001b[0m, in \u001b[0;36mHTTPResponse.stream\u001b[0;34m(self, amt, decode_content)\u001b[0m\n\u001b[1;32m    626\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    627\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_fp_closed(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp):\n\u001b[0;32m--> 628\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    630\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m data:\n\u001b[1;32m    631\u001b[0m             \u001b[38;5;28;01myield\u001b[39;00m data\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/urllib3/response.py:567\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[0;34m(self, amt, decode_content, cache_content)\u001b[0m\n\u001b[1;32m    564\u001b[0m fp_closed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclosed\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m    566\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_error_catcher():\n\u001b[0;32m--> 567\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fp_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m fp_closed \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    568\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    569\u001b[0m         flush_decoder \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/urllib3/response.py:533\u001b[0m, in \u001b[0;36mHTTPResponse._fp_read\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m buffer\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    532\u001b[0m     \u001b[38;5;66;03m# StringIO doesn't like amt=None\u001b[39;00m\n\u001b[0;32m--> 533\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp\u001b[38;5;241m.\u001b[39mread()\n","File \u001b[0;32m/opt/conda/lib/python3.10/http/client.py:472\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    470\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_conn()\n\u001b[1;32m    471\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 472\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    473\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength:\n\u001b[1;32m    474\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_conn()\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}],"execution_count":1},{"cell_type":"markdown","source":"SpeechT5 expects audio data to have a sampling rate of 16 kHz, so make sure the examples in the dataset meet this requirement.\n\nSpeechT5 要求音频数据的采样率为 16 kHz，因此请确保数据集中的样本满足此要求。","metadata":{}},{"cell_type":"code","source":"dataset=dataset.cast_column(\"audio\", Audio(sampling_rate=16000))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-15T01:27:53.416180Z","iopub.execute_input":"2025-08-15T01:27:53.417121Z","iopub.status.idle":"2025-08-15T01:27:53.877649Z","shell.execute_reply.started":"2025-08-15T01:27:53.417085Z","shell.execute_reply":"2025-08-15T01:27:53.876428Z"}},"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dataset\u001b[38;5;241m=\u001b[39m\u001b[43mdataset\u001b[49m\u001b[38;5;241m.\u001b[39mcast_column(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maudio\u001b[39m\u001b[38;5;124m\"\u001b[39m, Audio(sampling_rate\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m16000\u001b[39m))\n","\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined"],"ename":"NameError","evalue":"name 'dataset' is not defined","output_type":"error"}],"execution_count":2},{"cell_type":"markdown","source":"# Preprocess the data\nLet's begin by defining the model checkpoint to use and loading the appropriate processor:","metadata":{}},{"cell_type":"code","source":"from transformers import SpeechT5Processor\n\ncheckpoint=\"microsoft/speecht5_tts\"\nprocessor=SpeechT5Processor.from_pretrained(checkpoint)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T01:37:47.774319Z","iopub.execute_input":"2025-08-26T01:37:47.774636Z","iopub.status.idle":"2025-08-26T01:37:58.971960Z","shell.execute_reply.started":"2025-08-26T01:37:47.774601Z","shell.execute_reply":"2025-08-26T01:37:58.971241Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"preprocessor_config.json:   0%|          | 0.00/433 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6dea5b1711b7402b88b6d78acfd1eaf4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/232 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"aca95e7eab944a40a17b36b64d172f3c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"spm_char.model:   0%|          | 0.00/238k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d1cc0f13a4d548a8910179feccadbd21"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"added_tokens.json:   0%|          | 0.00/40.0 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"49faa6b0a0f84b30a9c27e918eb8f411"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/234 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"89788609475d43daa680f990c8b27e81"}},"metadata":{}}],"execution_count":1},{"cell_type":"markdown","source":"# Text cleanup for SpeechT5 tokenization\n\nThe dataset examples contain `raw_text` and `normalized_text` features. When deciding which feature to use as the text input, consider that the SpeechT5 tokenizer doesn't have any tokens for numbers. In `normalized_text` the numbers are written out as text. Thus, it is a better fit, and we recommend using `normalized_text` as input text.\n\nBecause SpeechT5 was trained on the English language, it may not recognize certrain characters in the Dutch dataset. If left as is, these characters will be converted to `<unk>` tokens. However, in Dutch, certain characters like `à` are used to stress syllables. In order to preserve the meaning of the text, we can replace this character with a regular `a`.\n\nTo identify `unsupported` tokens, extract all unique characters in the dataset using the SpeechT5 Tokenizer which works with chatacters as tokens. To do this, write the `extract_all_chars` mapping function that concatenates the transcriptions from all examples into one string and converts it to a set of characters. Make sure to set batched=True and batch_size=-1 in dataset.map() so that all transcriptions are avaliable at once for the mapping function.\n# 为 SpeechT5 分词进行文本清理\n数据集中的样本包含 raw_text（原始文本）和 normalized_text（标准化文本）这两个特征。在决定使用哪个特征作为文本输入时，需要考虑到 SpeechT5 的分词器（tokenizer）没有任何用于表示数字的词元（token）。在 normalized_text 中，数字被以文本的形式书写了出来（例如，将 “123” 写为 “one hundred twenty-three”）。因此，它是一个更合适的选择，我们推荐使用 normalized_text 作为输入文本。\n\n由于 SpeechT5 是在英语语言数据上训练的，它可能无法识别荷兰语数据集中的某些特定字符。如果对这些字符不做处理，它们将被转换为 <unk>（未知）词元。然而，在荷兰语中，像 à 这样的字符被用来重读音节。为了保留文本的原始含义，我们可以将这个字符替换为常规的 a。\n\n为了识别出那些不被支持的词元，我们需要使用 SpeechT5 分词器来提取数据集中所有唯一的字符。该分词器将单个字符作为词元进行处理。为此，你需要编写一个名为 extract_all_chars 的映射函数（mapping function），该函数会将所有样本中的转录文本拼接成一个长字符串，然后将其转换为一个字符集合。请确保在调用 dataset.map() 时设置 batched=True 和 batch_size=-1，这样映射函数就能一次性处理所有的转录文本。","metadata":{}},{"cell_type":"code","source":"def extract_all_chars(batch):\n    all_text=\" \".join(batch[\"normalized_text\"])\n    vocab=list(set(all_text))\n    return {\"vocab\": [vocab], \"all_text\":[all_text]}\n\nvocabs=dataset.map(\n    extract_all_chars,\n    batched=True,\n    batch_size=-1,\n    keep_in_memory=True,\n    remove_columns=dataset.column_names,\n)\n\n\ntokenizer=processor.tokenizer\n\ndataset_vocab=set(vocabs[\"vocab\"][0])\ntokenizer_vocab={k for k, _ in tokenizer.get_vocab().items()}","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.261788Z","iopub.status.idle":"2025-08-13T08:18:59.262188Z","shell.execute_reply.started":"2025-08-13T08:18:59.261984Z","shell.execute_reply":"2025-08-13T08:18:59.262003Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Deal with Special Characters\n\nNow, we have two sets of characters: one with the vocabulary from the dataset and one with the vocabulary from the tokenizer. To identify any unsupported characters in the dataset, we can take the difference between these two sets. The resulting set will contain the characters that are in the dataset but not in the tokenizer.\n\nTo handle the unsupported characters identified in the previous step, define a function that maps these characters to valid tokens. Note that spaces are already replaced by _ in the tokenizer and do not need to be handled separately.","metadata":{}},{"cell_type":"code","source":"print(dataset_vocab-tokenizer_vocab)\n\n\nreplacements=[\n    (\"à\", \"a\"),\n    (\"ç\", \"c\"),\n    (\"è\", \"e\"),\n    (\"ë\", \"e\"),\n    (\"í\", \"i\"),\n    (\"ï\", \"i\"),\n    (\"ö\", \"o\"),\n    (\"ü\", \"u\"),\n]\n\n\ndef cleanup_text(inputs):\n    for src, dst in replacements:\n        inputs[\"normalized_text\"]=inputs[\"normalized_text\"].replace(src, dst)\n    return inputs\n\ndataset=dataset.map(cleanup_text)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.263529Z","iopub.status.idle":"2025-08-13T08:18:59.263921Z","shell.execute_reply.started":"2025-08-13T08:18:59.263715Z","shell.execute_reply":"2025-08-13T08:18:59.263734Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Now that we have deal with special characters in the text, it's time to shift focus to the audio data.","metadata":{}},{"cell_type":"markdown","source":"# Checking Speakers\n\nThe VoxPopuli dataset includes speech from multiple speakers, but how many speakers are represented in the datasets? To determine this, we can count the number of unique speakers and the number of examples each speaker contributes to the dataset. With a total of 20968 examples in the dataset, this information will give us a better understanding of the distribution of speakers and examples in the data.","metadata":{}},{"cell_type":"code","source":"from collections import defaultdict\nimport matplotlib.pyplot as plt\n\nspeaker_counts=defaultdict(int)\n\nfor speaker_id in dataset[\"speaker_id\"]:\n    speaker_counts[speaker_id]+=1\n\nplt.figure()\nplt.hist(speaker_counts.values(), bins=20)\nplt.ylabel(\"Speakers\")\nplt.xlabel(\"Examples\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.265270Z","iopub.status.idle":"2025-08-13T08:18:59.265823Z","shell.execute_reply.started":"2025-08-13T08:18:59.265599Z","shell.execute_reply":"2025-08-13T08:18:59.265621Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Cleaning Data\n\nThe histogram reveals that approximately one-third of the speakers in the dataset have fewer than 100 examples, while arounf ten speakers have more than 500 examples. To improve training effficiency and balance the dataset, we can limit the data to speakers with between 100 and 400 examples.\n\nNote that some speakers with few examples may actually have more audio available if the examples are long. However, determining the total amount of audio for each speaker requires scanning through the entire dataset, which is a time-consuming process that involves loading and decoding each audio file. As such, we have chosen to skip this step here.","metadata":{}},{"cell_type":"code","source":"def select_speaker(speaker_id):\n    return 100<=speaker_counts[speaker_id]<=400\n\ndataset=dataset.filter(select_speaker, input_columns=[\"speaker_id\"])\n\n# Let's check how many speakers remain\nprint(len(set(dataset[\"speaker_id\"])))\n\n# Let's see how many examples are left\nprint(len(dataset))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.267216Z","iopub.status.idle":"2025-08-13T08:18:59.267641Z","shell.execute_reply.started":"2025-08-13T08:18:59.267428Z","shell.execute_reply":"2025-08-13T08:18:59.267449Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Speaker embeddings\n\nTo enable the TTS model to differentiable between multiple speakers, we will need to create a speaker emebeddings for each example. The speaker embedding it an additional input into the model that captures a particular speaker's voice characteristics. To generate these speakers embeddings, use the pre-trained spkrec-xvect-voxceleb model from SpeechBrain. Create a function **create_speaker_emebdding()** that takes an input audio waveform and outputs a 512-element vector containing the corresponding speaker embedding. It's important to note that the speechbrain/spkrec-xvect-voxceleb model was trained on English speech from the VoxCeleb dataset, whereas the training examples in here are in Dutch. While we believe that this model will still generate reasonable speaker embeddings for our Dutch dataset, this assumption may not hold true in all cases.\n\nFor optimal results, we recommend training an X-vector model on the target speech first. This will ensure that the model is better able to capture the unique voice characteristics present in the Dutch language.","metadata":{}},{"cell_type":"code","source":"import os\nimport torch\nfrom speechbrain.pretrained import EncoderClassifier\n\nspk_model_name=\"speechbrain/spkrec-xvect-voxceleb\"\n\ndevice=\"cuda\"\nspeaker_model=EncoderClassifier.from_hparams(\n    source=spk_model_name,\n    run_opts={\"device\": device},\n    savedir=os.path.join(\"/tmp\", spk_model_name),\n)\n\n\ndef create_speaker_embedding(waveform):\n    with torch.no_grad():\n        speaker_embeddings=speaker_model.encode_batch(torch.tensor(waveform))\n        speaker_embeddings=torch.nn.functional.normalize(speaker_embeddings, dim=2)\n        speaker_embeddings=speaker_embeddings.squeeze().cpu().numpy()\n    return speaker_embeddings\n\n\ndef prepare_dataset(example):\n    audio=example[\"audio\"]\n    \n    example=processor(\n        text=example[\"normalized_text\"],\n        audio_target=audio[\"array\"],\n        sampling_rate=audio[\"sampling_rate\"],\n        return_attention_mask=False,\n    )\n    \n    # strip off thje batch dimension\n    example[\"labels\"]=example[\"labels\"][0]\n    \n    # use SpeechBrain to obtain x-vector\n    example[\"speaker_embeddings\"]=create_speaker_embedding(audio[\"array\"])\n    \n    return example","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.269129Z","iopub.status.idle":"2025-08-13T08:18:59.269557Z","shell.execute_reply.started":"2025-08-13T08:18:59.269327Z","shell.execute_reply":"2025-08-13T08:18:59.269347Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Verify the processing is correct by looking at a single example, the speaker embeddings should be a 512-element vector, and the labels should be a log-mel spectrogram with 80 mel bins.","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\n\nprocessed_example=prepare_dataset(dataset[0])\nprint(list(processed_example.keys()))\nprint(processed_example[\"speaker_embeddings\"].shape)\n\nplt.figure()\nplt.imshow(processed_example[\"labels\"].T)\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.271047Z","iopub.status.idle":"2025-08-13T08:18:59.271506Z","shell.execute_reply.started":"2025-08-13T08:18:59.271254Z","shell.execute_reply":"2025-08-13T08:18:59.271276Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# applying the processing function to the entire dataset\ndataset=dataset.map(prepare_dataset, remove_columns=dataset.column_names)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.272706Z","iopub.status.idle":"2025-08-13T08:18:59.273100Z","shell.execute_reply.started":"2025-08-13T08:18:59.272893Z","shell.execute_reply":"2025-08-13T08:18:59.272917Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"We will see a warning saying that some examples in the dataset are longer than the maximum input length the model can handle(600 tokens). Remove those examples from the dataset. Here we go even further and to follow for larger batch sizes we remove anything over 200 tokens.","metadata":{}},{"cell_type":"code","source":"def is_not_too_long(input_ids):\n    input_length=len(input_ids)\n    return input_length<200\n\ndataset=dataset.filter(is_not_too_long, input_columns=[\"input_ids\"])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.274195Z","iopub.status.idle":"2025-08-13T08:18:59.274617Z","shell.execute_reply.started":"2025-08-13T08:18:59.274408Z","shell.execute_reply":"2025-08-13T08:18:59.274428Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"dataset=dataset.train_test_split(test_size=0.1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.275980Z","iopub.status.idle":"2025-08-13T08:18:59.276381Z","shell.execute_reply.started":"2025-08-13T08:18:59.276165Z","shell.execute_reply":"2025-08-13T08:18:59.276184Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Data collator\n\nWe want to combine multiple examples into a batch, we need to define a custom data collator. This collator will pad shorter sequences with padding tokens, ensuring that all examples have the same length. For the spectrogram labels, the padded portions are replaced with the special value `-100`. This special value instructs the model to ignore that part of the spectrogram when calculating the spectrogram loss.","metadata":{}},{"cell_type":"code","source":"from dataclasses import dataclass\nfrom typing import Any, Dict, List, Union\n\n@dataclass\nclass TTSDataCollatorWithPadding:\n    \n    processor: Any\n    \n    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n        input_ids=[{\"input_ids\":feature[\"input_ids\"]} for feature in features]\n        label_features=[{\"input_values\":feature[\"labels\"]} for feature in features]\n        speaker_features=[feature[\"speaker_embeddings\"] for feature in features]\n        \n        # collate the inputs and targets into a batch\n        batch=processor.pad(input_ids=input_ids, labels=label_features, return_tensors=\"pt\")\n        \n        # replace padding with -100 to ignore loss correctly\n        batch[\"labels\"]=batch[\"labels\"].masked_fill(batch.decoder_attention_mask.unsqueeze(-1).ne(1),-100)\n        \n        #not used during fine-tuning\n        del batch[\"decoder_attention_mask\"]\n        \n        # round down target lengths to multiple of reduction factor\n        if model.config.reduction_factor>1:\n            target_lengths=torch.tensor([len(feature[\"input_values\"]) for feature in label_features])\n            target_lengths=target_lengths.new(\n                [length-length%model.config.reduction_factor for length in target_lengths]\n            )\n            max_length=max(target_lengths)\n            batch[\"labels\"]=batch[\"labels\"][:, :max_length]\n        \n        # also add in the speaker embeddings\n        batch[\"speaker_embeddings\"]=torch.tensor(speaker_features)\n        \n        return batch","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.277702Z","iopub.status.idle":"2025-08-13T08:18:59.277978Z","shell.execute_reply.started":"2025-08-13T08:18:59.277843Z","shell.execute_reply":"2025-08-13T08:18:59.277858Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"In SpeechT5, the input to the decoder part of the model is reduced by a factor 2. In other words, it throws away every other timestep from the target sequence. The decoder then predicts a sequence that is twice as long. Since the original target sequence length may be odd, the data collator makes sure to round the maximum length of the batch down to be a multiple of 2.","metadata":{}},{"cell_type":"code","source":"data_collator=TTSDataCollatorWithPadding(processor=processor)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.279287Z","iopub.status.idle":"2025-08-13T08:18:59.279579Z","shell.execute_reply.started":"2025-08-13T08:18:59.279445Z","shell.execute_reply":"2025-08-13T08:18:59.279459Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Train the model\n\nThe `use_cache=True` option is incompatible with gradient checkpointing. Disable it for training.","metadata":{}},{"cell_type":"code","source":"from transformers import SpeechT5ForTextToSpeech\n\nmodel=SpeechT5ForTextToSpeech.from_pretrained(checkpoint)\nmodel.config.use_cache=False\nprint(model.config)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.281167Z","iopub.status.idle":"2025-08-13T08:18:59.281624Z","shell.execute_reply.started":"2025-08-13T08:18:59.281376Z","shell.execute_reply":"2025-08-13T08:18:59.281434Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer\n\ntraining_args=Seq2SeqTrainingArguments(\n    output_dir=os.getenv(\"WANDB_NAME\"),\n    per_device_train_batch_size=4,\n    gradient_accumulation_steps=8,\n    learning_rate=1e-5,\n    warmup_steps=50,\n    max_steps=100,\n    gradient_checkpointing=True,\n    fp16=True,\n    evaluation_strategy=\"steps\",\n    per_device_eval_batch_size=2,\n    save_steps=50,\n    eval_steps=50,\n    logging_steps=25,\n    report_to=\"wandb\", # or report_to=\"tensorboard\"\n    run_name=os.getenv(\"WANDB_NAME\"),\n    load_best_model_at_end=True,\n    greater_is_better=False,\n    label_names=[\"labels\"],\n    push_to_hub=False,\n)\n\ntrainer=Seq2SeqTrainer(\n    args=training_args,\n    model=model,\n    train_dataset=dataset[\"train\"],\n    eval_dataset=dataset[\"test\"],\n    data_collator=data_collator,\n    tokenizer=processor,\n)\n\ntrainer.train()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.282599Z","iopub.status.idle":"2025-08-13T08:18:59.282992Z","shell.execute_reply.started":"2025-08-13T08:18:59.282789Z","shell.execute_reply":"2025-08-13T08:18:59.282808Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"trainer.evaluate()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.284776Z","iopub.status.idle":"2025-08-13T08:18:59.285174Z","shell.execute_reply.started":"2025-08-13T08:18:59.284967Z","shell.execute_reply":"2025-08-13T08:18:59.284986Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"processor.push_to_hub(os.getenv(\"WANDB_NAME\"))\ntrainer.push_to_hub(os.getenv(\"WANDB_NAME\"))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.286576Z","iopub.status.idle":"2025-08-13T08:18:59.286970Z","shell.execute_reply.started":"2025-08-13T08:18:59.286765Z","shell.execute_reply":"2025-08-13T08:18:59.286783Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Inference","metadata":{}},{"cell_type":"code","source":"from transformers import pipeline()\n\npipe=pipeline(\"text-to-speech\", model=os.getenv(\"WANDB_NAME\"), device='cuda')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.288164Z","iopub.status.idle":"2025-08-13T08:18:59.288512Z","shell.execute_reply.started":"2025-08-13T08:18:59.288322Z","shell.execute_reply":"2025-08-13T08:18:59.288339Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"text=\"hallo allemaal, ik praat nederlands. groetjes aan iedereen!\"\n\nexample=dataset[\"test\"][304]\nspeaker_emebeddings=torch.tensor(example[\"speaker_emebddings\"]).unsqueeze(0)\n\nforward_params={\"speaker_embeddings\": speaker_embeddings}\noutput=pipe(text, forward_params=forward_params)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.289728Z","iopub.status.idle":"2025-08-13T08:18:59.290017Z","shell.execute_reply.started":"2025-08-13T08:18:59.289877Z","shell.execute_reply":"2025-08-13T08:18:59.289894Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from IPython.display import Audio\nAudio(output['audio'], rate=output['sampling_rate'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T08:18:59.290966Z","iopub.status.idle":"2025-08-13T08:18:59.291254Z","shell.execute_reply.started":"2025-08-13T08:18:59.291118Z","shell.execute_reply":"2025-08-13T08:18:59.291132Z"}},"outputs":[],"execution_count":null}]}